Information, stored in databases, is a key competitive
advantage of many companies. However, this importance does not
imply that managers will view data as a strategic resource or
that they have the experience to interpret business processes
in terms of data management.
One way to overcome this corporate inexperience is
to establish a data quality system - a controlled and robust
process that executes the measurement, analysis and improvement
of data quality. Yet, often the most difficult problem in practice
is to get a data quality system up and running, and accepted as
worthwhile by senior management.
Based on my experience, practice and research into
data quality, I recommmend four steps to initiate the implementation
of a successful system: (i) establish data quality position, (ii)
formulate a data quality policy, (iii) determine objectives and
(iv) obtain management and employee commitment. For each of these
steps I highlight ways to ensure the best chances to succesfully
manage data quality.
1. Motivating Factors and Benefits
Businesses face many problems, some of which are
caused, or at least, aggravated by poor data quality management.
I briefly describe here the core motivating factors relating to
Reducing management failures: making the right decision
Since many management decisions are based on a quantitative
analysis, from MIS, customer surveys, accounting systems or otherwise,
it is indisputable that poor quality data can have a devastating
consequence. The data could be incomplete, inconsistent or just
plain wrong. For example, First Financial Management Corp.of Atlanta,
US had to restate its earnings for the first nine months of 1991
after discovering that a subsidiary lost track of some records
after changing its accounting systems.
Similarly, targetted marketing based on demographics
can be inefficient or even counter-productive if the data underlying
the strategy are faulty in some way. In one case, a large New
York casino that routinely keeps in touch with its customers via
direct mail, a process that depends on up-to-date, reliable mailing
lists, estimated that only about 80% of the data in the customer
list was accurate.
Thus, any improvement in the corporate MIS database
will lead to more informed management decison making and strategy
Raising product and service quality
Because product and service design, manufacturing
and delivery are often based on an IT infrastructure of systems
and data, a organisation's competitive position can be at risk
if data quality is poor. Federal Express, for example, reportedly
made significant improvements to the quality of data in its COSMOS
database system, and gained a substantial market share and competitive
In 1995 the US Information Industry Association started
development on a new 26-letter ID numbering system for stock exchange
symbols. The scheme was an answer to the problem of multiple,
sometimes inconsistent, identification codes from multiple international
sources. In this example, incorrectly interpreting information
from a stock exchange could effect the competitive position of
an investor. The converse is also true: an exchange supplying
uninterpretable or "error-inducing" data could contribute
to impaired reputation of the exchange.
In another, more stark case, Information Systems
Management, Spring, 1993, reported on an operational data
entry error that led to a loss of $500 million for Salomon Brothers.
Less quantifiable, is the opportunity cost or loss of customer goodwill associated with every mistake or minor error due to poor-quality data. Examples of these "minor errors" are
_ male customer addressed as "Mrs" because of an error in the gender code
_ product delivered to an old address, because there is no data enrichment process
_ wrong account credited because of a data input error, customer has cheque bounced because funds were insufficient
_ cross-sell opportunity
missed because trigger demograpgic data was wrong or missing
According to 1994 research by the Gartner Group,
the justification for investing in integrated customer information
lies in the improved data quality, customer service, and trend
analysis. When launching a direct marketing campaign "companies
utilizing a data warehouse to perform this task have reported
an increase from a response rate of two to three percent to a
rate of 20 to 30 percent." Restating from the data quality
perspective, poor data quality could be responsible for your company
under-achieving in direct marketing by a factor of ten. Note,
however, that implementing a data warehouse in itself doesn't
raise data quality.
Companies can be sued or fined for wrong reporting
or misconduct, the causes for which simply may be polluted databases.
Organisations are also subject to specific data quality laws.
In 1992, Corporate Computing cited inaccurate and incomplete
data contained in consumer credit reports as one of the reasons
in a lawsuit against TRW. The damage to corporate image or standing
can also be affected by mistakes caused by data quality problems.
Many enterprises, particularly those extending credit,
need to detect, assess and control fraud or credit exposure. Detecting
fraud or bad credit is often a highly data-dependent activity:
identifying spending patterns, totaling expourse to individuals
at a customer level, identifying insolvent individuals or companies.
All of these tasks are made harder with incomplete, inaccurate
or missing data.
Reducing major clean-ups
It is not uncommon for corporations to lack a data
quality policy or focus (although the authors know of organisations
like Reuters, who at one time had a Director of Data Quality
Projects and AT&T who, in various guises, had a dedicated
data quality group). For many companies there is often a short
period of intense activity on data clean-up or scrubbing, usually
after several prominent quality issues have surfaced and impacted
their business, to "fix" a particular problem. This
activity is usually expensive and time consuming. Moreover, several
months later a new ad-hoc project may be started to achieve something
similar. This patchwork data quality approach, and the expense
of rectifying the effects of data quality problems, can be substantial
when viewed over a timeframe of 2-5 years.
2. A Data Quality System
It is a platitude to state that information is a
key competitive advantage of companies. However, this self-evident
truth does not imply that managers view data as a strategic resource
or that they have the experience to interpret business processes
in terms of data management.
This lack of experience can be diffused by the establishment
of a data quality system - a controlled and robust process
that executes the measurement, analysis and improvement
of data quality. This kind of system or process is familiar to
many managers of operations or manufacturing. A major benefit
is that this approach tends to eliminate root causes, permanently
fix problems and will be less expensive over the long run. The
trade-offs are shown in Table 1.
|Ad-hoc approach||Data Quality System|
|Improvement to data||High||Medium|
|Improvement to process||None||Medium|
|Improvement to data||Low||High|
|Improvement to process||None||High|
Another benefit of a data quality system is its transparency to
senior management, in terms of the contribution it brings to an
An important point to digest is that a data quality system is
not a software tool, rather it is a management discipline. An
effective system can be built without any expensive or specialized
Steps to build a data quality system
I present here the basic steps needed to initiate a data quality system or to "get started". These represent the typical first steps, as well as the chief hurdles, to a successful data quality project, both from a cost-benefit angle and from an organizational inertia perspective. These steps are:
(i) establish data quality position
(ii) formulate a data quality policy
(iii) determine objectives
(iv) obtain management and employee commitment
There are different views on the execution sequence of the four
steps. Many companies may decide to measure data quality levels
first before investing time in any other activity, so at least
there is some notion of what problems they face, how much they
are costing and by how much they are likely to improve. Some corporations
may take a top-down approach and first mandate a data quality
(or perhaps, information management) policy. The latter would
be more likely when information is the firm's product (as for
a financial information feeder) or when senior management are
already convinced that better data quality will lead to a major
A logical sequence is first to understand the size and nature
of the problem, then to draft a policy, set objectives and lastly
present to senior managemnet for concurrence. Policy and objectives
can be fine tuned after management feedback.
3. Establish current position
The single most important and revealing step is to measure exisiting
quality levels and to quantify their associated costs. Because
a company may only want an indication of the current position,
sampling techniques could be allied with either a manual review,
simple "health check" programs (written by the IT department)
and some data collection on costs. Table 2 shows an example of
a first cut indication.
|Error Type||Level %|
|Missing IC/passport number||4.9|
This approach, that is, to defer the use of specialized data quality
tools, has the advantage of giving quick and relatively inexpensive
feedback to management.
During this step it may also be useful to review software quality
processes, product quality assurance, data management procedures
and other related company standards or procedures. This will give
managers an idea of where they stand in terms of the maturity
of data quality process management and how much they need to be
Data collection and analysis
Data collection involves the quantifying of the unit costs of
quality failure, computation of the quality levels (failure rates)
and for non-quantifiable costs, fixing a level of business criticality
that the data quality aspect demands. With these numbers it will
be possible to calculate an overall cost and impact of data quality
problems. The data needed to calculate rates, costs etc will primarily
belong to your company and hence should at the very least be available,
even if they may be difficult to tease out or to convert to a
useful unit of measure.
During the data analysis, it is best to use the concept of dimensions
(problem types) discussed by many data quality researchers, so
as to gain a top-level understanding of the nature of problems
and likely root causes. A single gross error rate, or quality
level, while perhaps useful to report to senior management will
not be very helpful as a diagnostic tool.
At this stage it could well be worth doing some limited "benchmarking"
of your data quality performance against other companies in your
industry, so as to give a competitive context to your problems
and allow you to understand the severity of your quality levels.
Table 3 gives a basic benchmark (because the data is not industry
specific it may not be so useful).
Some advice on data collection is given:
_ Many useful rates, ratios and costs may already known by the financial control or accounts department, for example, unit labour costs (useful in calculating cost of rework), unit computer processing costs, average cost of a sales call, avreage profit per customer, average revenue by product.
_ External sources of information, such as vendor newsletters, user group conference proceedings, trade journals, may be useful in comparing data quality levels.
_ Sources of qualitative information, such as employees, customers,
industry analysts, consultants, distributors and agents may have
a deep if not so precise knowledge of data quality causes, effects,
and relative importance.
Identify current documented practices
The purpose of this step is to understand what is being done today in your organisation officially or unofficially. Much of this input will be illustrative of why there are data quality problems or will give ideas on where to make improvements. Even if a data quality system never existed in an integrated form before there will be some documentation relating to data quality, including
_ software documentation (particularly on database schemas, data dictionaries, data entry systems)
_ user procedures
_ accounting rules and uses
_ information security manuals amd policies
_ complaint handling procedures
As part of this exercise, management may decide to catalogue the
SWOT (strength, weakness, opportunity and threat) to the business
from the current data quality position. An example is shown below:
_ Service and Quality Culture
_ Innovative and receptive Management
_ Widespread usage of service indicators (akin to data quality measures)
_ "Instant results" culture not supportive of data quality effort
_ Fragmented systems and databases work against high data quality
_ No data quality policy
_ Market differentation is increasingly service quality, heavily dependent on data quality
_ Sales and amrketing are increasingky data driven, hence data quality dependent
_ Existing inhouse expertise in data quality
_ Competition is assessed to be far behind
_ Short-termism rules out the upfront costs of data quality investment
_ Initiatives from competitors
_ Pressure to deliver new technology fast erodes software quality and hence data quality
A qualitative analysis
You have read that informal sources of information, such as employees,
may be a valuable aid in the establishment of your current position
and practices. One way to structure this task is by means of a
user survey or questionnaire. A survey can help to crystallise
issues and also gives staff a channel for their feedback (the
latter should raise data quality awareness and motivation among
staff). Senior management is also likely to be galvanized by
seeing in black and white some of the issues facing their staff.
What should be in a data quality survey? I suggest a template
|Criticality||Rate your data elements according to their importance. For example:|
_ Required: data is required for legal/other reasons
_ Important: data is needed to deliver product on time with superior service
_ Nice to have: data is never needed, but may be useful to exceed customer expectations
_ Not important: data does not increase customer satisfaction, is not needed for other reasons
|Accuracy, Timeliness||Rate your data elements according to their accuracy or timeliness|
|When and Where||Consider situations to help you identify where and how data corruption occurs: (list common situations such as System Conversion/Integration, Inadequate testing, Inconsistent data across systems)|
|Control Mechanisms||Give examples of the controls that can be put into place to improve data quality. Ask users to think about how better controls may reduce errors.|
|Reliability||Identify the system in which the "best" information (most reliable, most timely) is held.|
|General Issues||What is the major data quality problem you face today? |
Identify any issues related to specific processes, that may be related to the quality of data.
What is the most important change you would like to see that could improve data quality?
4. Formulate a data quality policy
A template data quality policy is given:
Management shall define for new or existing products, processes or services
_ data quality objectives, such as performance, costs and losses.
_ data quality factors affecting market position
Line managers shall implement, monitor, and manage controls over
their data. These controls shall maintain appropriate data quality
levels based upon the specific conformance criteria set by the
business. A separate assurance unit should track the execution
of the policy; the quality system should be internally audited
and evaluated on a regular basis.
Such a data quality policy can be incorporated into an information
security, data management, software engineering or service policy,
or may be kept separate and hence more visible. Some of the aspects
of a data quality policy that a company should consider are:
Organizations will need to identify or develop, and document data
quality methods and procedures, which will allow line mangers
to follow an agreed process. Effort needs to be placed on educating
and training line staff, in addition to training quality "experts".
Relevant international and national standards should be noted
In order to achieve accountability, a business must establish
and document data ownership for all databases. Data owners would
be responsible for the quality level of their database, among
other administration tasks, such as approval of access to the
data and data classification. Without ownership the statistics
showing quality levels will become simply more wasted trees.
Firms may also consider appointing dedicated data quality champions,
as Computerworld noted in Sept 4, 1995:
Data stewards are becoming ever more important if not critical
positions in the management of corporate data. Companies have
come to recognize that data is a critical asset, resulting in
the growth of sophisticated data access tools and data warehousing.
Businesses also recognize that ensuring the quality of data is
equally important. The data steward manages data assets to optimize
their quality, utility, accessibility and reusability. A wide
range of tasks is involved, including establishing naming standards,
criteria for data quality and retention, security specifications
and definitions of standard data entities and attributions. Ideally,
a data steward is assigned to each major data subject area. A
data steward should have a variety of technical, business and
5. Determine the objectives
Once the current position is established, a firm should set measurable
data quality goals, either in terms of percentages levels or monetary
values. For example, a firm could try to halve its overall data
quality indicator from 5% to 3%, or it could try to reduce its
data quality related losses from £2 million per annum to
£500,000 per annum. Since it may be difficult to set a single
data quality rate, one approach is to classify the criticality
of data and set targets for each class. Table 5 demonstrates this
|Class of Data Elements||Current Quality Level*||Target Quality Level*|
(eg social security number)
|Customer Service Related
(eg residence type)
Several factors will influence the setting of goals, particularly
the overriding corporate priority assigned to objectives such
_ Cost reduction: a prime goal may be to target quality problems that bring the greatest payback in terms of reduced cost. Since the frequency/level and cost of individual problem types/dimensions have been calculated, focussing on the key areas for improvement presents few problems.
_ Ease of improvement: certain problems may be fixed quickly, for example, adding range checks on input screens. Other problems may require more involved and prolonged implementation, for example, the introduction of a data quality tool. Quick fixes have the advantage of helping to demonstrate to management the tangible value of the data quality project.
_ Competitive Factors: another major goal will be to achieve corporate customer service or product quality goals, usually with reference to competitive position or industry norms. If the link between data quality and service/product quality is well understood, or at least can be estimated, quality levels can be set based on this relationship.
_ Regulation and risk: if a certain class of problems is likely to expose the corporation to regulatory or serious financial risk, then obviously these data quality issues would be tackled first.
_ Differing business need: an enterprise may run several business
lines and these may be more or less sensitive to data quality
levels. The business with higher dependence on accurate data would
be the obvious consideration for constructing the objective.
During the implementation stages, these goals should be tracked
closely, so that progress is known, and focus is not lost on the
rasion d'etre of the data quality system.
6. Obtain management and employee commitment
The success of any project depends on the backing of senior business
management and employee buy-in. Often senior managers will not
have a ready appreciation of data quality, either because data
quality is too "technical", too abstract or has not
been publicized enough in business magazines and literature. However,
because poor data quality can be translated into losses (monetary,
goodwill, sales opportunity) it should be relatively easy to persuade
business managers that some action should be taken.
Likewise, cast in terms of improved customer service, raised morale,
and competitive advantage, it should be easy to sell data quality
to employees. This buy-in is enhanced, and the data quality system
much improved, if line staff are involved during the project.
The most persusive statistics are the outputs of the step to establish
the current data quality position since these will quantify the
effects of doing nothing. This can highlight, in tangible and
easily understandable terms, the effect of data quality on that
Based on my experience, practice and research into data quality,
I have recommmended four steps to initiate the implementation
of a successful data quality effort: (i) establish data quality
position, (ii) formulate a data quality policy, (iii) determine
objectives and (iv) obtain management and employee commitment.
For each of these steps I have highlighted some of the stumbling
blocks and success factors that could make or break a data quality
project. Establishing a data quality system - a process that executes
the measurement, analysis and improvement of data quality - is
a leap forwards for companies grappling with poor data management.
The preparatory activites described here make that task easier.